DocumentCode :
2541094
Title :
Relevance Vector Machine Based Gear Fault Detection
Author :
He, Chuangxin ; Li, Yanming ; Huang, Yixiang ; Liu, Chengliang ; Fei, Shengwei
Author_Institution :
Sch. of Mech. Eng., Shanghai Jiao Tong Univ., Shanghai, China
fYear :
2009
fDate :
4-6 Nov. 2009
Firstpage :
1
Lastpage :
5
Abstract :
Recently, condition monitoring of machinery has become global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. In this paper, a novel fault detection method based on relevance vector machine (RVM) is proposed for gear condition monitoring. Empirical results demonstrated that, using similar training time, the RVM model has shown comparable generalization performance to the popular and state-of-the-art support vector machine (SVM), while the RVM requires dramatically fewer kernel functions and needs much less testing time. The results lead us to believe that the RVM is a more powerful tool for on-line fault detection than the SVM.
Keywords :
condition monitoring; fault location; gears; maintenance engineering; support vector machines; gear condition monitoring; gear fault detection; machine availability; machinery; maintenance costs; relevance vector machine; support vector machine; Availability; Condition monitoring; Costs; Fault detection; Gears; Kernel; Machinery; Productivity; Support vector machines; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2009. CCPR 2009. Chinese Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4199-0
Type :
conf
DOI :
10.1109/CCPR.2009.5344002
Filename :
5344002
Link To Document :
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